Anomaly Detection

Overview

Anomaly detection identifies unusual patterns, outliers, and anomalies in data that deviate significantly from normal behavior, enabling fraud detection and system monitoring.

When to Use

• Detecting fraudulent transactions or suspicious activity in financial data
• Identifying system failures, network intrusions, or security breaches
• Monitoring manufacturing quality and identifying defective products
• Finding unusual patterns in healthcare data or patient vital signs
• Detecting abnormal sensor readings in IoT or industrial systems
• Identifying outliers in customer behavior for targeted intervention

Detection Methods

• Statistical: Z-score, IQR, modified Z-score
• Distance-based: K-nearest neighbors, Local Outlier Factor